10 Top Tips For Improving Developer Engineering Productivity

In this article, we will explain how productivity maps across an organization and is impacted and improved by various actors – from top-level executives to individuals in the engineering and QA teams who build and test all parts of a software product. If you’re a manager, don’t stop after reading your part because now more than ever, enabling your engineers with the right tools can bring huge productivity gains.

If you’re a developer and you’re looking for an extended list of tools, take a look at these articles for further suggestions for the best developer experience and SDLC tools.

Let’s face it – productivity starts at the top. Delivering software solutions to meet a genuine market need is rarely straightforward. The marketplace evolves faster than most can track, competition is often hidden in unexpected corners, and time-to-market can make or break your entire venture. That’s why clear, well-informed decisions at the top aren’t just helpful-they’re critical. Without them, your team risks pouring valuable hours into initiatives that ultimately don’t align with real customer needs or long-term business goals.

Marketing, product, and engineering teams have long collaborated under frameworks like Lean or Agile. These collaborations ensure (or at least should ) that when you green-light a project or pivot in strategy, it’s backed by data and user insight. Ambiguity or overreach in decision-making can lead to scope creep, causing your developers to chase moving targets and waste time on rework.

As a manager, you should aim for early validation and decisive actions. These will keep the budget on track, allow the development to stay focused, and deliver features that genuinely resonate with the users, whether individuals or organizations. Thinking in terms of OKRs (objectives and key results) can benefit your team and is discussed in more detail here. The RICE Scoring Model (reach, impact, confidence, effort) can be a valuable tool for shaping your strategy, and the WSFJ (weighted shortest job first) can also be a powerful ally by prioritizing initiatives based on data rather than guesswork.

You might be familiar with the saying: “Culture eats strategy for breakfast.” The way you structure your engineering team and the rituals you establish can be just as crucial as the tools you use.

Consider looking into some of the following team structures:

1. Functional (or specialized) teams, where each team focuses on a specific function—front-end, back-end, QA, DevOps, etc. This structure allows team members to specialize deeply, but communication overhead between teams often grows, and handoffs can slow projects down.
2. Cross-functional teams allow developers, designers, and QA to collaborate closely on shared objectives. Decision-making can be accelerated, and alignment can be quickly achieved. However, they can become inefficient when you, as a manager or executive, don’t provide a clear way for those teams to collaborate with other cross-functional teams.
3. Feature teams, often present in larger products, bring together people to focus on a single feature or a set of closely related features touching only certain areas of the codebase. They emphasize clear ownership but can lead to siloed areas if not managed carefully.
4. Matrix or hybrid Teams: A combination of functional and cross-functional teams in which people report to both a functional manager (ensuring that objectives are met dead-on and up to standard) and a project manager, who can ensure that the developers’ efforts align with delivery goals.
5. The Spotify model is an autonomous approach to scaling Agile, first introduced at Spotify. Small cross-functional teams called squads act as mini-startups within an organization, while chapters bring together employees with similar skills, and guilds form informally around employees’ interests. The model boasts high autonomy, but needless to say, it can be a bit complicated to maintain.

Obviously, there is no single “correct” setup, as the best approach often depends on the complexity of your product, the size of your company, and the kind of collaboration you want to foster.

After exploring the industry’s options for various team structures and ways of working, focus on designing a workflow that suits your organization’s resources and consider how you will measure your teams’ efficiency. It’s useless to insist on a model that might seem good on paper but fails to prove itself in your own context.

What about your team’s “rituals” and ways of working?

The best idea is to keep rituals lean and purposeful. Short daily standups help sync progress, while sprint retros encourage honest feedback. However, watch out for meeting overload—it can drain energy and derail coding time. Clear onboarding and up-to-date documentation should be highly valued, as they show newcomers the ropes quickly, preventing guesswork and rework. Modern teams are now accelerating this by employing generative AI tools to scan large codebases and automatically create documentation, onboarding wikis, and auto-document API endpoints and interfaces.

Engineers and product owners alike, everyone can be easily overwhelmed when a task at hand is either too big or poorly defined, or worse, both. To increase productivity, consider setting specific, measurable goals, as advocated in Agile methodologies and frameworks like OKRs—you will be empowering engineers to work autonomously while aligning their efforts with the broader product vision. This approach leads to faster feedback loops, improved accountability, and measurable outcomes. Traditionally, tools like Kanban boards and sprint tracking dashboards can operationalize these principles, ensuring that each chunk of work contributes transparently to the project’s overall success.

In Milestone, we use AI to create actionable, real-time metrics for decision-makers, revealing where your team struggles and where it excels.

If you want to get closer to technology, there’s good news and bad news. The bad news is that a plethora of tooling is already incorporated into even the most basic workflows, and some of those can be challenging to understand. The good news is that AI is revolutionizing the domain, not only by simplifying current flows but, in many cases, by plugging in to give visibility and metrics and automatically suggest improvements.

Consider these key areas:

• CI/CD Pipelines: Tools like GitLab CI/CD, CircleCI, and Spinnaker automate code integration and deployment. AI-enhanced systems now analyze pipeline performance, predict bottlenecks, and recommend optimizations. For example, platforms like Harness use machine learning to detect anomalies in deployment patterns, while DeepCode tries to identify vulnerabilities and provide fixes for them when code is pushed to your repo.
• Infrastructure as Code (IaC): Tools such as Terraform, OpenTofu, AWS CloudFormation, and Ansible allow you to define infrastructure through code, ensuring consistency and repeatability. The declarative nature of the code written in those “languages” is an excellent candidate for analysis by AI tools such as context-aware/context-trained LLMs, as offered by OpenAI, Amazon Bedrock, and even DeepSeek. These tools can help identify misconfigurations and automate compliance checks, thereby reducing the risk of errors. You can try to “roll your own” by prompting and contextualizing those tools directly or using tools that plug them into your repositories to automatically create reports, explanations, or reviews. Just be aware of your company’s policy on sharing your data with third parties, and always be careful about managing secrets.
• GitOps: GitOps leverages tools like Argo CD and Flux to manage deployments by tracking code changes in version control systems. Your team can increase its productivity by deploying an AI assistant, such as the one from Akuity, which can monitor these deployments, provide real-time insights, automate rollback procedures, and even suggest improvements based on historical data.
• Naming Conventions: Consistent naming conventions in repositories, branches, and infrastructure components minimize confusion. AI-driven code analysis tools, such as GitHub Copilot and SonarQube, can flag deviations from established patterns, ensuring better maintainability and easier navigation across your codebase.

Finally, this list of tips would not be complete if we didn’t mention that the everyday life of a developer (a domain called Devex) can be drastically improved by using the right tooling. Luckily (as it is developers who build everything we mentioned above), tools for development have existed long before anything else. It’s beyond the scope of this article to give an exhaustive list of tools for the dev, as these often depend on the task at hand or the domain a developer is working in. But here’s a non-exhaustive list of tools relevant in 2025 to help with developer engineering productivity, roughly split across various software engineering domains:

• Backend and Networks: Insomnia, Postman, and Charles offer API design solutions, API testing, and convenient proxy and rewiring services that can drastically improve engineering productivity, especially when dealing with microservice architectures. Most of JetBrains IDEs now ship their latest versions with built-in AI code assistant support. Replit is an AI-enabled, web-based IDE with an integrated AI assistant for coding, dependency management, and deployment.
• UI/UX design: Figma has lately introduced AI features in their tried and true prototyping environment while tooling like Sketch and Visily emerge as strong contenders that put intuition and AI-powered workflows first.
• Knowledge Management: Notion, a top player in the game of knowledge management, has long been shipping with an agent able to summarize pages or meeting notes, while Confluence with Atlassian Intelligence simplifies search across your knowledge base, and Slack’s AI bots can autogenerate conversation summaries and highlight key action items.
• Security and Compliance: ZAP is a powerful attack proxy that can be used to examine web applications, while BurpGPT is the latest addition to Burp Suite’s scanning capabilities for security flaws.
• General Purpose LLMs: ChatGPT, Claude, and Perplexity have emerged, among others, as platforms for assisting with reasoning, code generation, and research, respectively. While those tools are great and easy to access, it’s harder to integrate them into your environment, which has led to the rapid expansion of the tailored, AI-enabled apps ecosystem.

1. Define clear objectives and KPIs: Set precise, measurable goals (using OKRs or similar frameworks) that align with your business vision. Metrics provide the baseline for monitoring progress across your SDLC.
2. Align team structures with measurable outcomes: Choose a team model-be it cross-functional, feature-based, or hybrid-that best supports streamlined workflows and quantifiable deliverables.
3. Standardize lean team rituals: Keep meetings purposeful and data-driven. Daily standups and sprint retrospectives should focus on key metrics and rapid issue resolution.
4. Break projects into quantifiable chunks: Decompose work into smaller, measurable tasks. This granularity enhances accountability and ensures each piece contributes directly to overall performance.
5. Optimize onboarding and documentation: Streamline onboarding processes and maintain living documentation. Fast, measurable ramp-up times are essential for reducing wasted cycles.
6. Streamline CI/CD pipelines: Implement modern CI/CD tools integrated with AI to track deployment metrics, identify bottlenecks, and drive efficiency in code integration and delivery.
7. Leverage IaC and GitOps for consistency: Use tools like Terraform and Argo CD, enhanced with AI analytics, to enforce consistency and continuously measure deployment quality.
8. Invest in domain-specific AI-enhanced tools: Adopt tools across the backend, UI/UX, and security that offer built-in metrics and AI-driven insights, ensuring that every step is measurable and optimized.
9. Utilize general-purpose LLMs: Integrate conversational AI like ChatGPT or Claude to quickly generate insights and code while tracking performance metrics to refine workflows.
10. Foster a data-driven culture of continuous improvement: Regularly review metrics, feedback, and AI-suggested optimizations. This approach ensures that your processes remain agile, effective, and aligned with strategic goals.

By embracing these tips, you can ensure that every layer of your software engineering process is not only streamlined but also clearly measurable-giving you a comprehensive, real-time view of your SDLC efficiency.